Big data analytics involves the analysis of large and complex data sets that may frustrate traditional data analysis techniques. Although many organizations now produce or have access to large amounts of data, such as sensor data, organization data, or transaction records, big data analytics involves many challenges to organizations that may attempt to collect, store, and analyze large data sets with the limited computing resources and/or storage resources available to the organization. Even analyses of very large data sets which may be successful may involve more computing resources and may take more time than can be afforded by the organization.
Data models may be created as a tool to analyze large quantities of data to perform big data analytics. Data models may describe the behaviors observed within an input data set, such that the data model may be used to compare and classify new data against the observed behaviors of the input data set. However, like other big data analytics processes, data modeling techniques can be time intensive and resource intensive to develop, deploy, and execute. Moreover, many data model building activities require the creation of many different data models using many different data sets, in order to better analyze the data and discover the patterns and variables underlying the data. Unfortunately, many organizations lack the time, computing resources, infrastructure, or scalability to perform the processing and storage required to create and use data models based on large amounts of data. As a result, large quantities of data available to organizations are often discarded without any analysis, or are misunderstood by organizations without the resources to perform a meaningful analysis of the data.